Logistics and industrial automation increasingly rely on accurate localization to support and control manual and automated processes, with applications ranging from “smart things” through effective tracking and assistance solutions to robots such as automated guided vehicles (AGVs).
UWB technology has been advocated as a localization solution suitable for asset tracking applications. Such applications are concerned with maintaining a centralized database of assets and their storage locations in a warehouse, hospital, or factory. When using UWB technology, assets, such as pallets, equipment, or also people may be equipped with tags that emit UWB signals at regular intervals. These signals may then be detected by UWB sensors installed in the warehouse, hospital, or factory. A central server then uses the UWB signals detected by the UWB sensors to compute the tag's location and update the centralized database.
Mobile robots are increasingly used to aid task performance in both consumer and industrial settings. Autonomous mobile robots in particular offer benefits including freeing workers from dirty, dull, dangerous, or distant tasks; high repeatability; and, in an increasing number of cases, also high performance. A significant challenge in the deployment of mobile robots in general and autonomous mobile robots in particular is robot localization, i.e., determining the robot's position in space. Current localization solutions are not well suited for many mobile robot applications, including applications where mobile robots operate in areas where localization such as that provided by global navigation satellite systems (GNSS) is unreliable or inoperative, or applications that require operation near people.
Using current UWB localization solutions for robot localization would not enable a mobile robot to determine its own location directly. Rather, a robot equipped with a tag would first emit an UWB signal from its location, UWB sensors in its vicinity would then detect that UWB signal and relay it to a central server that would then compute the mobile robot's location, and then this location would have to be communicated back to the robot using a wireless link. This type of system architecture invariably introduces significant communication delays (e.g., latency) for controlling the mobile robot. This communication architecture also results in a relatively higher risk of lost signals (e.g., due to wireless interference) and correspondingly lower system robustness, which makes it unsuitable for many safety-critical robot applications (e.g., autonomous mobile robot operation). Furthermore, in this architecture the maximum number of tags and the tag emission rate (i.e., the localization system's update rate) are invariably linked because in these systems multiple UWB signals currently do not overlap. This results in limited scalability for a given tag emission rate (i.e., the system can only support a limited number of tags in parallel). In addition, if a higher tag emission rate or redundancy is required, then a smaller number of tags will need to be used. In addition, with such an architecture, the maximum update rate for determining the position of tags is inversely proportional to the number of tags. This is unsuitable for situations where a large number of objects need to be tracked with a high update rate.
Another localization system proposed in the prior art uses mobile transceivers that communicate with stationary transceivers through the two-way exchange of UWB signals. Two-way communication with a stationary transceiver enables a mobile transceiver to compute the time-of-flight between itself and the stationary transceiver. In this architecture, communication between mobile transceivers and stationary transceivers must be coordinated, such that communications do not interfere. Knowledge of the time-of-flight to three or more stationary transceivers enables each mobile transceiver to compute its relative location within an environment using trilateration. Because each mobile transceiver communicates with each stationary transceiver, the update rate of the system is inversely proportional to the number of mobile transceivers and to the number of stationary transceivers. This architecture is therefore not suitable for systems where a large number of objects must be localized at a high frequency (e.g., tracking a group of robots, where position measurements are used in the robots' control loops to influence the robots' motions), where a mobile transceiver's position or identity should be kept private (e.g., tracking people), where both transceiver redundancy and high update frequency are desired (e.g., safety critical applications such as positioning systems for vehicles), or in multipath environments, which require a maximum of transceivers to help disambiguate multipath signals, where a high update frequency and a large number of tracked objects are desired (e.g., robot warehouses).